Graphical representation of financial information

ABSTRACT

A method comprises displaying a visible representation of a plurality of stocks in a stock market by a respective plurality of regions that are arranged based on a plurality of similarity values between a respective plurality of pairs of the stocks. Each of the plurality of similarity values is based on a respective correlation between a respective first time series indicating, for each of a plurality of time intervals, an aggregate level of messaging in postings of messages for a respective first stock in its respective pair of the stocks and a respective second time series indicating, for each of the plurality of time intervals, an aggregate level of messaging in postings of messages for a respective second stock in its respective pair of the stocks. Each of the regions is user-selectable to retrieve information from a message board associated with its respective one of the plurality of stocks.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.13/507,436, filed Jun. 28, 2012, pending, which is a continuation ofU.S. application Ser. No. 13/136,157, filed Jul. 25, 2011, now U.S. Pat.No. 8,228,332, which is a continuation of U.S. application Ser. No.13/066,078, filed Apr. 6, 2011, now U.S. Pat. No. 7,990,383, which is acontinuation of U.S. application Ser. No. 12/925,691, filed Oct. 27,2010, now U.S. Pat. No. 7,928,982, which is a continuation of U.S.application Ser. No. 12/592,176, filed Nov. 20, 2009, now U.S. Pat. No.7,830,383, which is a continuation of U.S. application Ser. No.11/820,859, filed Jun. 21, 2007, now U.S. Pat. No. 7,626,586, which is acontinuation of U.S. application Ser. No. 11/431,801, filed May 9, 2006,now U.S. Pat. No. 7,239,317, which is a continuation of U.S. applicationSer. No. 10/388,258, filed Mar. 13, 2003, now U.S. Pat. No. 7,046,248,which claims the benefit of U.S. Provisional Application No. 60/365,125,filed Mar. 18, 2002, and U.S. Provisional Application No. 60/403,862,filed Aug. 15, 2002. The above-identified applications are herebyincorporated by reference into the present application.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to methods and systems for organizing anddisplaying financial information.

2. Description of the Related Art

Fidelity Investments' Web site provides a visual representation ofstocks in the stock market, which is branded as “Map of the Markets”.Each of the stocks is represented by a rectangular shape. An aggregateof the rectangular shapes is also rectangular. Though all of the stocksare represented in a compact form, it is unclear how a stock representedby one rectangle specifically relates to stocks represented byneighboring rectangles.

PCQuote.com introduced a graphical representation of a single equity,branded as “Sniper”. The representation has a bulls eye design comprisedof 13 sectors, each sector representing an area of reconnaissance. Theareas of reconnaissance include: stock flow which indicates arelationship between a number of shares that traded on the bid sideversus a number of shares traded on the offer side, quadrantdistribution which indicates a total volume of shares traded distributedacross four quadrants to show what prices are attracting the mostactivity, price rotation which numerically indicates when price rotationactivity favors buyer or sellers, relative price rotation which visuallyindicates when price rotation activity favors buyers or sellers, timerotation which measures the acceptance of higher or lower prices,relative time rotation which shows a current time period bias, “totarget” which indicates when a stock price is within ½ point of aforecast high or low price, “to exit” which indicates a most extremestop-out price, buy/sell imbalance which measures where a majority oftrades are occurring, look-out signal which indicates a day's directionfor an equity, gatekeeper which provides a logical stop-out price, stockand market direction which points out a path of least resistance, and“center of the scope” which indicates whether buyers or sellers aredominant across the board.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is pointed out with particularity in the appendedclaims. However, other features are described in the following detaileddescription in conjunction with the accompanying drawings in which:

FIG. 1 is a flow chart of an embodiment of a method of relating aplurality of financial items by a tree;

FIG. 2 is a flow chart of an embodiment of a method of determiningregions to represent tree-related financial items;

FIG. 3 is a flow chart of an embodiment of a method of determiningangles to define the regions;

FIG. 4 is a flow chart of an embodiment of a method of determining radiito define the regions; and

FIG. 5 shows an example of regions which represent tree-relatedfinancial items;

FIGS. 6, 7(A-B) and 8 are a flow chart of an embodiment of a method offurther organizing the financial item information;

FIG. 9 shows an example of a rearrangement of the regions in FIG. 5;

FIG. 10 is an example of a tree which relates fourteen stocks A-N to abase stock S;

FIG. 11 is a first example of regions to represent the fourteen stocksA-N and the base stock S related by the tree in FIG. 10; and

FIG. 12 is a second example of regions to represent the fourteen stocksA-N and the base stock S related by the tree in FIG. 10.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Disclosed herein are improved methods of organizing and representinginformation pertaining to a plurality of financial items. Examples ofthe financial items include, but are not limited to, stocks, equities,bonds, commodities, currencies, options, futures, mutual funds, marketindices, sector indices, and investment trusts. The financial items areorganized into a tree based on a degree of similarity between pairs ofthe items. The financial items may be further organized by determining adepth-first search of the tree having an optimum value associatedtherewith. The associated value of a depth-first search is based upon anorder of considering financial items in the tree, and similarity valuesbetween pairs of financial items in the tree.

Based on the tree and optionally the optimal depth-first search,parameters which define regions to represent the financial items aredetermined. Each region has a controlled area based upon a predeterminedvalue, herein referred to as a weight, associated with the financialitem. Examples of weight values associated with any herein-mentionedfinancial item include, but are not limited to, a price of the financialitem, a trading volume of the financial item, a market capitalizationassociated with the financial item, a dollar-measured trading volume ofthe financial item, a book value associated with the financial item, netassets of the financial item, earnings associated with the financialitem, and other properties of the financial item.

Preferably, each region has an area monotonically related, and morepreferably proportional, to its weight. For example, consider a firstregion representing a first financial item with a first weight value,and a second region representing a second financial item with a secondweight value. If the first weight value is greater than the secondweight value, the first region has a first area greater than a secondarea of the second region.

The weight values need not be determined for cases in which each of thefinancial items either is unweighted or has the same weight value. Inthese cases, the hereinafter-described computations can be simplified byassigning a weight of one to each of the financial items. A plurality ofregions, each having the same area, results in these cases.

The regions may be visibly displayed to provide a visible representationof the financial information, and/or used to provide an input interfaceto allow a user-initiated selection of a portion of the financialinformation. Optionally, the regions may be printed to produce a hardcopy representation of the financial information.

Each non-root financial item in the tree is represented by acorresponding concave region such as an annulus sector. In describingshapes in this patent application, the term “annulus sector” is meant tobe synonymous with “sector of an annulus”. As such, an approximatelycircular annulus sector is meant to describe an approximate sector of acircular annulus. An approximately circular annulus sector is definableby a first line segment, a second line segment, a first approximatelycircular arc, and a second approximately circular arc. The firstapproximately circular arc is at least partially definable by a firstfocus point and a first radius. The second approximately circular arc isat least partially definable by a second focus point and a secondradius. Preferably, the first focus point and the second focus point arelocated substantially at the same point. It is further preferred thatthe first radius and the second radius differ so that the region has aradial width.

The first approximately circular arc is further definable by a firststarting angle and either a first ending angle or a first arc angle. Thesecond approximately circular arc is further definable by a secondstarting angle and either a second ending angle or a second arc angle.Preferably, the first arc angle and/or the second arc angle are non-zeroso that the region has an angular width. Also preferably, the firststarting angle is substantially the same as the second starting angle,the first ending angle is substantially the same as the second endingangle, and the first arc angle is substantially the same as the secondarc angle.

The first line segment and the second line segment are preferablyoriented substantially radially with respect to a point. Preferably,this point is located substantially at the first focus point and thesecond focus point. In this case, it is also preferred that the firstline segment be oriented substantially radially with respect to thepoint at an angle substantially the same as the first starting angle andthe second starting angle, and that the second line segment be orientedsubstantially radially with respect to the point at an anglesubstantially the same as the first ending angle and the second endingangle.

The root financial item may be represented by a substantially convexshape. Preferably, the substantially convex shape is at least partiallydefined by at least a portion of an approximate circle. Examples of thesubstantially convex shape having this preferred form include, but arenot limited to, an approximate sector of a circle, and approximately anentire circle.

It is noted that use of the terms “approximate” and “approximately” forthe herein-disclosed regions, shapes, and curves include non-perfectrepresentations of said regions, shapes, and curves using a displaydevice, a hard copy device such as a printer, and/or an input device.For example, a pixel-based display device can display a plurality ofdiscrete pixels to approximate any of the herein-disclosed regions,shapes, and curves. As another example, a display device may distort anintended region, shape, or curve to produce an approximation thereof.Examples of this distortion include, but are not limited to, adistortion due to pixel aspect ratio, a distortion due to a non-planardisplay screen, and a distortion due to rasterization.

Approximations of regions, shapes, and curves may also be generated insoftware or firmware. For example, a curve may be represented by apiecewise approximation. An example of a piecewise approximation of acurve includes, but is not limited to, a piecewise linear approximation.Further examples include a curve being approximated by a simplifiedequation therefor, and a curve being approximated by a plurality ofdisplay points. Examples of ways to approximate a shape or a regioninclude, but are not limited to, using a plurality of points toapproximate the shape or region, and using a polygon to approximate theshape or region.

Use of discrete parameter values to represent a region, shape, or curvealso may result in an approximation thereof. For example, a circular arcmay be represented by an integral center coordinate, an integral radius,an integral start angle, and an integral arc width. In this case, andother cases, either round-off or truncation of parameter values tocomply with a discrete representation results in an approximation of adesired region, shape, or curve.

It is noted that a shape need not be precisely convex to besubstantially convex. Examples of insubstantial concavities in asubstantially convex shape include, but are not limited to, those whichmay result from an approximation of the shape or a curve which at leastpartially defines the shape, and those present where endpoints of twocurves imprecisely meet.

Before proceeding, a review of graph-related terminology is provided. Agraph is definable by a set of nodes and a set of edges joining orassociating different pairs of distinct nodes. The edges in the graphmay be either directed or undirected. It is noted that alternativeterminology may be used to describe the graph. Examples of synonyms of“node” include, but are not limited to, “vertex” and “state”. Examplesof synonyms of “edge” include, but are not limited to, the terms “arc”and “link”. Therefore, the herein-disclosed methods, articles,apparatus, and examples should not be limited by the selectedterminology used to describe the graph.

A first node is said to be adjacent to a second node if there is an edgefrom the first node to the second node. A path is definable by asequence of nodes wherein each consecutive pair of nodes in the sequenceis adjacent.

A tree is a graph having a unique path from a designated node, called aroot node, to each of its other nodes. If the tree is undirected, thenany of its nodes can be designated to be the root node. An undirectedtree can be made into a directed tree by directing all edges away fromthe designated root node.

Each node in a directed tree, except for the root node, is a child nodeof a unique parent node from which an edge is directed thereto. Nodeshaving the same parent node are called siblings. Nodes of a directedtree with no children are called leaf nodes. Nodes having at least onechild are called internal nodes.

A level number of a node is defined as the number of edges in the pathbetween the node and the root node. The height of the tree is thelargest level number of any node.

If each internal node of a rooted tree has m children, the tree iscalled an m-ary tree. If m=1, the tree is unary. If m=2, the tree isbinary. If m=3, the tree is ternary.

FIG. 1 is a flow chart of an embodiment of a method of relating aplurality of financial items by a tree. Each of the financial items isrepresented by a corresponding node of the tree. It is preferred thatthe tree is non-unary. By being non-unary, the tree has at least oneinternal node with two or more child nodes. It is also preferred thatthe tree has a height of at least two. It is further preferred that thetree has more leaf nodes than a number of child nodes emanating from itsroot node.

As indicated by block 20, the method comprises selecting one of thefinancial items as a base item. The base item is assigned as the rootnode of the tree. Typically, the base item is one whose relationshipwith other financial items is of interest. For example, an investor maybe interested in how a particular stock relates to other stocks. In thiscase, the particular stock is selected as the base item.

As indicated by block 22, the method comprises determining a firstplurality of similarity values between the base item and a plurality ofother financial items. Each of the first plurality of similarity valuesindicates a degree of similarity or correlation between the base itemand a corresponding one of the other financial items.

As indicated by block 24, the method comprises determining a secondplurality of similarity values between a corresponding plurality ofpairs of the other financial items. Each of the second plurality ofsimilarity values indicates a degree of similarity or correlationbetween a corresponding pair of the other financial items.

The similarity values may be based on a correlation between tradingbehaviors of two financial items. For example, the similarity values maybe based on either a correlation coefficient between a price behavior oftwo financial items or a correlation coefficient between a volumebehavior of two financial items. A more specific example is thesimilarity value being based on a correlation coefficient of theend-of-day price data for two stocks over a year. In general, thecorrelation coefficient may be determined over any time length of data(e.g. a day, a week, a month, a year-to-date, a year or multiple years)with any suitable sampling interval of data (e.g. one minute, 5 minutes,30 minutes, one day, one week, one month or one year) for any type ofdata (e.g. price, volume or dollar volume).

As indicated by block 26, the method comprises determining an optimumpath tree based on the first plurality of similarity values and thesecond plurality of similarity values. The optimum path tree indicates arespective optimum path between the base item and each of the otherfinancial items.

The optimum path between the base item and another financial item has anoptimum function value of similarity values between the base item andthe other financial item. Examples of the function whose optimum valuedictates the optimum path include, but are not limited to, a sum ofsimilarity values between the base item and the other financial item,and a product of similarity values between the base item and the otherfinancial item. To determine an optimum product of similarity values, anadditive optimum path algorithm may be performed on a logarithm of thesimilarity values. For example, if the similarity values are numericalvalues between 0% and 100%, then an additive minimum path algorithm maybe performed on a negative logarithm of the non-zero similarity values,e.g. −log(similarity value), to determine a maximum product ofsimilarity values.

Each similarity value may be set to a large constant if a correlationcoefficient between its two financial items is less than or equal tozero, and set to the negative logarithm of the correlation coefficientif the correlation coefficient is greater than zero. In this case, anadditive minimum path algorithm is performed using the aforementionedsimilarity values. The large constant is selected so that adjacent pairsof financial items in each minimum path have a correlation coefficientgreater than zero.

As is known in the art of network algorithms, examples of algorithms tocompute the shortest paths include, but are not limited to, Dijkstra'salgorithm and Floyd's algorithm. Those having ordinary skill can reviewshortest path algorithms on pp. 123-127 of A. Tucker, AppliedCombinatorics, Second Edition, John Wiley & Sons, 1984.

As an alternative to block 26, alternative types of spanning trees maybe determined based upon the first plurality of similarity values andthe second plurality of similarity values. For example, an optimumspanning tree such as a minimum spanning tree may be determined. Theoptimum spanning tree has an optimum function value of similarityvalues. Examples of the function whose optimum value dictates theoptimum spanning tree include, but are not limited to, a sum ofsimilarity values, and a product of similarity values. To determine anoptimum product of similarity values, an additive optimum spanning treealgorithm may be performed on a logarithm of the similarity values.

As is known in the art of network algorithms, examples of algorithms tocompute a minimum spanning tree include, but are not limited to,Kruskal's algorithm and Prim's algorithm. Those having ordinary skillcan review minimum spanning trees on pp. 127-131 of A. Tucker, AppliedCombinatorics, Second Edition, John Wiley & Sons, 1984.

As indicated by block 30, the method comprises representing the tree ina computer-readable form using a computer-readable medium. Variouscomputer-readable data structures can be used to represent the tree inthe computer-readable form using the computer-readable medium. Based onthe computer-readable form of the tree, regions to represent thefinancial items can be determined as described with reference to FIGS. 2to 5.

FIG. 2 is a flow chart of an embodiment of a method of determiningregions to represent tree-related financial items. Each financial itemis represented by a corresponding node of a tree. For each non-root nodein the tree (as indicated by block 40), a first value associated withthe node is determined (as indicated by block 42). The first value isbased on (W+C1)/C2, wherein W denotes a weight of the node, C1 denotes acumulative weight of all descendants of the node, and C2 denotes acumulative weight of all descendants of a parent of the node.

For each non-root node in the tree (as indicated by block 44), a secondvalue associated with the node is determined (as indicated by block 46).The second value is equal to a product of the first values for allnon-root nodes in a tree-defined path from the root node to the node.

As indicated by block 50, the method comprises determining angles todefine the regions based on the second values. The angles may be basedon an overall angular width of an aggregation of the regions, hereindenoted by MAXIMUM_ARC_ANGLE, and an initial starting angle of theaggregation of the regions, herein denoted by FIRST_START_ANGLE. Ingeneral, the MAXIMUM_ARC_ANGLE value can be any value, such as less than180 degrees, about equal to 180 degrees, greater than 180 degrees butless than 360 degrees, or about equal to 360 degrees. If theMAXIMUM_ARC_ANGLE is equal to 360 degrees, the region representing theroot node is a circle. If the MAXIMUM_ARC_ANGLE is less than 360degrees, the region representing the root node is a sector of a circlehaving a start angle and an end angle.

The start angle for the root node is set to the FIRST_START_ANGLE value.The end angle for the root node is set to a sum of the FIRST_START_ANGLEvalue and the MAXIMUM_ARC_ANGLE value. A resulting arc angle for theroot node is equal to the MAXIMUM_ARC_ANGLE value.

An embodiment of a method of determining angles associated with non-rootnodes is shown in FIG. 3. As indicated by block 60, the method comprisesinitializing a variable, herein denoted by CHILD_START_ANGLE, of theroot node. The CHILD_START_ANGLE of the root node is initialized to beequal to FIRST_START_ANGLE. As indicated by block 62, a variable denotedas LEVEL is prepared to increment from 1 to a height of the tree. Asindicated by block 64, the method comprises performing acts for eachnode having a level number equal to the LEVEL variable. The actscomprise determining a start angle for the node (block 66), an arc anglefor the node (block 68), and an end angle for the node (block 70). Thestart angle is equal to the CHILD_START_ANGLE value of the parent of thenode. The arc angle is equal to a product of the second value of thenode and the MAXIMUM_ARC_ANGLE value. The end angle is equal to a sum ofthe start angle and the arc angle. Alternatively, the end angle may beequal to the start angle minus the arc angle.

The acts further comprise updating the CHILD_START_ANGLE of the parentof the node (block 72), and setting the CHILD_START_ANGLE of the node(block 74). The CHILD_START_ANGLE of the parent of the node is updatedto be equal to the end angle for the node. The CHILD_START_ANGLE of thenode is set to the start angle for the node. Optionally, the act inblock 74 is omitted if the node is a leaf node.

As indicated by block 76, flow of the method is directed back to block64 if there is a further node having a level number equal to the LEVELvariable. Otherwise, flow of the method is directed to block 80. Fromblock 80, if there is a further level to process, flow of the method isdirected to block 62 wherein the LEVEL variable is incremented. If thereare no further levels to process, the method is completed.

Referring back to FIG. 2, an act of determining radii to define theregions is performed as indicated by block 82. Preferably, the regionrepresenting the root node is definable by a single radius. Thus, if theMAXIMUM_ARC_ANGLE is equal to 360 degrees, the region representing theroot node may be a circle; and if the MAXIMUM_ARC_ANGLE is less than 360degrees, the region representing the root node may be a sector of acircle.

FIG. 4 is a flow chart of an embodiment of a method of determining radiito define the regions. The radii may be based on an overall radius ofthe aggregate of the regions, herein denoted by MAXIMUM_RADIUS. Tosimplify subsequent computations, a constant K defined as theMAXIMUM_ARC_ANGLE times the square of the MAXIMUM_RADIUS, divided by thecumulative weight of all nodes in the tree, is determined.K=MAXIMUM_ARC_ANGLE*(MAXIMUM_RADIUS)²/(cumulative weight of all nodes inthe tree)

As indicated by block 90, an outer radius for the root node isdetermined. The outer radius for the root node is equal to the squareroot of: K times the weight of the root node divided by the arc anglefor the root node.

As indicated by block 92, a variable denoted as LEVEL is prepared toincrement from 1 to a height of the tree. As indicated by block 94, themethod comprises performing acts for each node having a level numberequal to the LEVEL variable. The acts comprise determining an innerradius for the node (block 96) and an outer radius for the node (block100). The inner radius for the node is determined to be equal to theouter radius for the parent of the node. The outer radius for the nodeis equal to the square root of: the sum of square of the inner radius ofthe node and K times the weight of the node divided by the arc angle forthe node. Optionally, if the node is a leaf node, the outer radius ofthe node can be determined to be equal to the MAXIMUM_RADIUS valuewithout performing the aforementioned computation.

As indicated by block 102, flow of the method is directed back to block94 if there is a further node having a level number equal to the LEVELvariable. Otherwise, flow of the method is directed to block 104. Fromblock 104, if there is a further level to process, flow of the method isdirected to block 92 wherein the LEVEL variable is incremented. If thereare no further levels to process, the method is completed.

Referring back to FIG. 2, an act of providing the regions to representthe nodes in the tree is performed, as indicated by block 110. Eachnon-root node is represented by a corresponding region definable withrespect to a common focus point by the start angle, end angle, innerradius, and outer radius. The root node is represented by a regiondefinable with respect to the common focus point by an outer radius, andoptionally a start angle and an end angle.

The resulting aggregation of regions has many desirable qualities. Forany tree configuration, the herein-disclosed method provides aone-to-one correspondence between the financial items and the regions.By one-to-one correspondence, it is meant that each financial item isrepresented by one and only one of the regions, and each of the regionsrepresents one and only one of the financial items.

Further, the aggregation of regions is contiguous for any treeconfiguration, which in the context of this disclosure is broadlyinclusive of describing the regions as being either neighboring oradjacent throughout the aggregation. For example, adjacent pairs ofregions which are spaced slightly apart are considered to be contiguous.

Still further, the aggregation of regions defines an identifiableboundary at its periphery for any tree configuration. If theMAXIMUM_ARC_ANGLE value is 360 degrees, the boundary comprises a circlehaving a radius equal to the MAXIMUM_RADIUS value. If theMAXIMUM_ARC_ANGLE value is less than 360 degrees, the boundary comprisesan arc having a radius equal to the MAXIMUM_RADIUS value, a start angleequal to the FIRST_START_ANGLE value, and an arc angle equal to theMAXIMUM_ARC_ANGLE value.

Yet still further, for any tree configuration, the herein-disclosedmethod provides a one-to-one correspondence between leaf nodes andregions adjacent the circle or arc boundary. By one-to-onecorrespondence, it is meant that all the leaf nodes are represented byregions adjacent the circle or arc boundary, and all regions adjacentthe circle or arc boundary represent leaf nodes. Thus, a user can easilyidentify the leaf nodes of the tree even if the branches of the treehave different lengths.

Also, for any tree configuration, the area of each region isproportional to the weight of its corresponding financial item. If thenodes are either equally weighted or unweighted, each of the regions hasthe same area for any tree configuration.

It is noted that there are various scenarios in which regions havingareas strictly unproportional to their weights have areas substantiallyproportional to their weights. Examples of sources of deviation in areaswhich still provide substantially proportional areas, but are notlimited to, approximations used to display the regions (see thediscussion herein of “approximate” and “approximately”), approximationsin the mathematical processes used to calculate the areas and/or theparameters which define the regions, and round-off and/or truncationerrors in the mathematical processes used to calculate the areas and/orthe parameters which define the regions.

It is noted that any values or parameters which are herein-described asbeing the same or equal can be slightly different, i.e. either about orapproximately the same, either about or approximately equal. In aparticular example, the inner radius of one or more nodes (determined inblock 96 in FIG. 4) can be modified to be slightly greater than theouter radius of its parent to mitigate a possibility ofradially-overlapping regions. Another way to mitigateradially-overlapping regions comprises modifying the outer radius of oneor more nodes to be slightly less than the inner radius of each of itschildren. In either of the above two examples, the inner radius and theouter radius are still considered to be about equal. Similarly, thestart angle and/or end angle of each region can be compressed toward thecenter of its region to mitigate a possibility of angularly-overlappingregions. In this case, angles of angularly adjacent regions are stillconsidered to be about equal. In any of the above examples, theresulting area of the region is still considered to be aboutmonotonically-related and/or about proportional to the weight of itscorresponding node.

An example of regions which represent equally-weighted tree-relatedfinancial items is shown in FIG. 5. A semicircular region 130 representsa base financial item (i.e. a root node of a tree). Circular annulussectors, a representative one indicated by reference numeral 132,represent other financial items whose relationship with the basefinancial item is of interest (i.e. non-root nodes of the tree).

All of the leaf nodes are represented by regions adjacent a semicirculararc 134 which partially defines a periphery of the aggregation of theregions. The regions are approximated by rounding the start angle andthe end angle to an integral number of degrees, and rounding the outerradius to an integral number of display units. All of the regions haveapproximately the same area.

The circular annulus sectors comprise a first circular annulus sectorregion 136 to represent a first financial item, a second circularannulus sector region 140 to represent a second financial item and athird circular annulus sector region 142 to represent a third financialitem. The second and third circular annulus sector regions 140 and 142are radially adjacent the first circular annulus sector region 136. Thesecond circular annulus sector region 140 is angularly adjacent thethird circular annulus sector region 142. A radial width of the secondcircular annulus sector region 140 differs from a radial width of thethird annulus sector region 142.

The relationship of the first, second and third financial items to thebase financial item is intuitively represented by the juxtaposition oftheir associated regions. By virtue of the first region 136 beingradially interposed between the region 130 and the regions 140 and 142,the base financial item is more similar to the first financial item thanto the second and third financial items.

The act of providing the regions may comprise outputting a signal todisplay the regions and/or outputting a signal to make the regionsuser-selectable, to provide a user interface. The at least one signal toprovide the user interface may be communicated by a computer datasignal. The computer data signal may be communicated via a computernetwork. Examples of the computer network include, but are not limitedto, an intranet, an internet and an extranet. The computer data signalmay include computer program code to assist in providing the userinterface. Of particular interest are signals representative of code ina markup language such as HTML (hypertext markup language).

The user-selectable regions can be provided in a variety of ways. Ofparticular interest is use of either a client-side image map or aserver-side image map to provide the user-selectable regions in relationto an image of the regions. Here, a user-selectable region can beprovided using HTML tags to approximate any of the herein-disclosedshapes, including convex shapes such as sectors and concave shapes suchas annular sectors. In particular, the polygon area definition in anAREA tag inside a MAP tag can be used to provide a user-selectableregion having one of the herein-disclosed shapes.

It is noted that a markup language improvement is contemplated whichwould provide an annular sector area definition and a sector areadefinition in an AREA tag inside a MAP tag. For example, a sector of acircle could be definable by an AREA tag having the following form:

<AREA SHAPE=“circle_sector”

COORDS=“x,y,x1,y1,x2,y2,x3,y3”

HREF=“URL”>

where (x,y) are coordinates of a center point of an arc-defining circle,(x1,y1) are coordinates of a point either on or collinear with a firstradial line segment, (x2,y2) are coordinates of a point either on orcollinear with a second radial line segment, (x3,y3) are coordinates ofa point either on or co-circular with the circular arc, and URL is acomputer address such as a uniform resource locator which is linked toin response to a user selection of the region. It is noted that theHREF=“URL” portion can replaced by one or more event codes (e.g.onclick, ondblclick, onmousedown, onmousemove, onmouseover, onmouseout,onmouseup) each followed by an associated function.

A sector of a circular annulus could be definable by an AREA tag havingthe following form:

<AREA SHAPE=“annulus_sector”

COORDS=“x,y,x1,y1,x2,y2,x3,y3,x4,y4”

HREF=“URL”>

where (x,y) are coordinates of a center point of a first arc-definingcircle and a second arc-defining circle, (x1,y1) are coordinates of apoint either on or collinear with a first radial line segment, (x2,y2)are coordinates of a point either on or collinear with a second radialline segment, (x3,y3) are coordinates of a point either on orco-circular with the first circular arc, (x4,y4) are coordinates of apoint either on or co-circular with the second circular arc, and URL isa computer address such as a uniform resource locator which is linked toin response to a user selection of the region. It is noted that theHREF=“URL” portion can replaced by one or more event codes (e.g.onclick, ondblclick, onmousedown, onmousemove, onmouseover, onmouseout,onmouseup) each followed by an associated function.

With this improvement, a user interface creator can more directlyimplement user-selectable regions having some of the herein-disclosedshapes.

The at least one signal is communicated by a waveform representativethereof through a communication medium. Examples of the waveform and thecommunication medium include, but are not limited to, an opticalwaveform through an optical medium, an electronic waveform through anelectronic medium, and an electromagnetic waveform through anelectromagnetic medium.

Based on the at least one signal, the regions may be displayed by adisplay device. Examples of the display device include, but are notlimited to, a computer monitor, a television, a liquid crystal display,a cathode ray tube, and a gas plasma display. For a computer datasignal, the at least one signal is received by a computer incommunication with the computer network. The computer generates adisplay signal to display the region on the display device.

Optionally, the regions representing the financial items may be arrangedto optimize or otherwise improve a function of similarity values betweenpairs of financial items represented by angularly-adjacent regions,while maintaining the radial-adjacency between adjacent financial itemsin the tree. FIGS. 6, 7(A-B) and 8 provide a flow chart of an embodimentof a method of further organizing the financial item information.

As indicated by block 152, the method comprises providing a depth-firstsearch sequence for considering financial items in the tree. An initialdepth-first search sequence may be provided by initializing, for each ofthe internal items having at least two child items in the tree, arespective sequence for considering its respective child items.

As indicated by block 154, the method comprises providing a memory whichis to indicate, for each level number from one to a height of the tree,a last-considered item associated with the level number in thedepth-first search sequence. The memory may be initialized to indicatethat no items have been considered at each level number from one to theheight of the tree.

As indicated by block 156, the method comprises considering a firstnon-root item in the depth-first search sequence as a current item. Asindicated by block 160, the method comprises determining if the currentitem is an internal item of the tree. If the current item is an internalitem of the tree, an act of determining if at least one of the leafitems had been previously considered in the depth-first search sequenceis performed (block 162).

If at least one of the leaf items had been previously considered in thedepth-first search sequence, a similarity value between the current itemand the last-considered item associated with the level number of thecurrent item is used to determine a value associated with thedepth-first search sequence (block 164). As indicated by block 166, thecurrent item is indicated as the last-considered item associated withthe level number of the current item.

Referring back to block 160, if the current item is not an internal itemof the tree, and thus is a leaf item of the tree, a subprocess isperformed for each level number L from the level number of the currentitem to the height of the tree (block 170). The subprocess comprisesdetermining if at least one of the leaf items had been previouslyconsidered in the depth-first search sequence (block 172) anddetermining if the last-considered item associated with level number Lis the same as the last-considered item associated with level number L-1(block 174). Since the root item is always the last-considered itemassociated with level number 0, the last-considered item associated withlevel number 1 always differs from the last-considered item associatedwith level number 0. Thus, block 174 may be skipped for L=1.

If at least one of the leaf items has been previously considered in thedepth-first search sequence and the last-considered item associated withlevel number L differs from the last-considered item associated withlevel number L-1, a similarity value between the current item and thelast-considered item associated with the level number L is used todetermine a value associated with the depth-first search sequence (block180). As indicated by block 182, the subprocess is performed one or moretimes for each level number indicated in block 170.

Each of the similarity values described with reference to blocks 164 and180 indicates a degree of similarity or correlation between acorresponding pair of financial items. The similarity values describedwith reference to blocks 164 and 180 may be the same as the secondplurality of similarity values used to determine the tree.Alternatively, the similarity values described with reference to blocks164 and 180 may differ from the second plurality of similarity valuesused to determine the tree.

As indicated by block 186, a subprocess is performed for each levelnumber L from the level number of the current item to the height of thetree. As indicated by block 190, the current item is indicated as thelast-considered item associated with the level number L. As indicated byblock 192, the subprocess is performed one or more times for each levelnumber indicated in block 186.

As indicated by block 194, a subsequent item in the depth-first searchsequence is considered as the current item. Flow of the method may bedirected back to block 156 until no subsequent items exist in thedepth-first search sequence. Optionally, block 156 may be modified tofurther iterate, after the final item, from the first non-root item tothe first leaf item. In this case, items from the first non-root item tothe first leaf item are considered twice.

The value associated with the depth-first search sequence is a functionof the similarity values identified in blocks 164 and 180. For example,the function may be based on a sum of the aforementioned similarityvalues, a product of the aforementioned similarity values, an average ofthe aforementioned similarity values such as either an arithmetic meanor a geometric mean of the aforementioned similarity values, a maximumof the aforementioned similarity values, or a minimum of theaforementioned similarity values.

The function may be partially evaluated each time a similarity value isidentified in blocks 164 and 180. Alternatively, the function may beevaluated after all of the similarity values have been identified inblocks 164 and 180 for all iterations of block 156. In general, thesimilarity values identified in blocks 164 and 180 may be used at anytime to evaluate the function and thus determine the value associatedwith the depth-first search sequence.

As indicated by block 196, the depth-first search sequence is modified.The depth-first search sequence may be modified by modifying thesequence for considering child items for each of at least one of theinternal items having at least two child items in the tree. Flow of themethod is directed back to block 154 to initiate the process ofdetermining a value associated with the modified depth-first searchsequence. The memory provided for indicating last-considered items inthe initial depth-first search sequence may be either different or thesame as the memory provided for indicating last-considered items for themodified depth-first search sequence.

In this way, values associated with a plurality of depth-first searchsequences of the tree are determined. Optionally, values associated withall possible depth-first search sequences of the tree may be determined.The number of possible depth-first search sequences of the tree is equalto the product of the factorial of each internal node's number ofchildren nodes.

Using an embodiment of the herein-disclosed method for some functions,two depth-first search sequences have the same value associatedtherewith if one has an order of considering leaf items which is thereverse of the other. Thus, in these and other cases, values associatedwith at most half of all possible depth-first search sequences of thetree may be determined. The depth-first search sequences which areconsidered may be limited by prohibiting reverse-equivalent sequencesfor considering child items of the root item. A branch-and-boundapproach, for example, may be used to limit consideration to less thanhalf of all possible depth-first search sequences.

Using another embodiment of the herein-disclosed method for somefunctions, two depth-first search sequences have the same valueassociated therewith if one has an order of considering leaf items whichis either a cyclic-equivalent or a reverse cyclic-equivalent of theother. For example, a sequence A-B-C-D has three cyclic-equivalentsequences B-C-D-A, C-D-A-B and D-A-B-C, and four reversecyclic-equivalent sequences D-C-B-A, A-D-C-B, B-A-D-C and C-B-A-D. Thus,in these and other cases, values associated with at most M/(2*N)sequences may be determined, where M is the number of all possibledepth-first search sequences of the tree, and N is the number of childitems of the root item. The depth-first search sequences which areconsidered may be limited by prohibiting cyclic-equivalent and reversecyclic-equivalent sequences for considering child items of the rootitem. A branch-and-bound approach, for example, may be used to limitconsideration to less than M/(2*N) sequences.

As indicated by block 200, the method comprises determining whichevaluated depth-first search sequence has an optimum value associatedtherewith. Depending on the function used to determine values associatedwith depth-first search sequences, this act may comprise eitherdetermining which depth-first search sequence has a maximum associatedvalue, or determining which depth-first search sequence has a minimumassociated value.

As indicated by block 202, a planar depiction of the tree is provided inwhich the items are ordered according to the depth-first search sequencehaving the optimum value. The planar depiction may comprise a pluralityof regions to visibly represent the items, wherein adjacent items in thetree are represented by radially-adjacent regions, and wherein pairs ofitems identified in blocks 164 and 180 for the depth-first searchsequence having the optimum value are represented by angularly-adjacentregions. In this case, if the acts in FIGS. 2-4 had been performed todetermine parameters defining the regions for a non-optimal arrangement,only the angles defining the regions need to be determined for theoptimal arrangement. Thus, the acts in FIG. 3 are performed again, wherethe order of the nodes considered in block 64 is dictated by the orderof the nodes in the optimum depth-first search sequence. The optimalarrangement does not affect the inner radius and outer radius of eachregion. For example, the regions in FIG. 5 may be rearranged as shown inFIG. 9 after performing the method described with reference to FIGS. 6-8and recalculating the angles as described with reference to FIG. 3.Alternatively, the acts in FIGS. 2-4 can be performed to determine allof the parameters for all of the regions.

If the depiction is to span an overall angular width of about 360degrees, it is preferred that block 156 be modified to further iterate,after the final item, from the first non-root item to the first leafitem.

Alternatively, the planar depiction may comprise a plurality of regionsto visibly represent the items, wherein adjacent items in the tree arerepresented by a visible edge between their corresponding regions, andwherein child items are ordered (either left-to-right, right-to-left,top-to-bottom, bottom-to-top, clockwise or counterclockwise, forexample) according to the sequence associated with the depth-firstsearch sequence having the optimum value.

An example is given to illustrate one embodiment of a method oforganizing financial information. As should be appreciated, the scope ofthe present disclosure is not to be limited by this example.

For purposes of illustration and example, consider a tree shown in FIG.10 which relates fourteen stocks A-N to a base stock S. The tree may bedetermined as described with reference to FIG. 1 based on a correlationof price behavior of the stocks. For each of the stocks, TABLE I showsan initial sequence for considering its child stocks in a depth-firstsearch of the tree. The initial sequence is used to provide an initialdepth-first search sequence for considering stocks in the tree.

TABLE I Sequence for considering Stock child stocks S A G N A B F B C DC — D E E — F — G H I M H — I J K L J — K — L — M — N —

TABLE II illustrates one embodiment of determining a value associatedwith the initial depth-first search sequence. Each row in TABLE IIrepresents an iteration associated with one of the stocks. From left toright, the columns in TABLE II indicate: (a) if at least one leaf stockhad been previously considered, (b) the current stock being consideredin the depth-first search sequence of the tree, (c) whether the currentstock is an internal stock or a leaf stock of the tree, (d) the levelnumber of the current stock, (e) a last-considered stock associated withlevel number 1, (f) a last-considered stock associated with level number2, (g) a last-considered stock associated with level number 3, (h) alast-considered stock associated with level number 4, and (i) whichsimilarity values are used to determine the value.

TABLE II At least one leaf Similarity stock values used in previouslyInternal function considered? Stock or leaf? Level 1 2 3 4 evaluation NoA Internal 1 A — — — — No B Internal 2 A B — — — No C Leaf 3 A B C C —Yes D Internal 3 A B D C (C,D) Yes E Leaf 4 A B D E (C,E) Yes F Leaf 2 AF F F (B,F) (D,F) (E,F) Yes G Internal 1 G F F F (A,G) Yes H Leaf 2 G HH H (F,H) Yes I Internal 2 G I H H (H,I) Yes J Leaf 3 G I J J (H,J) YesK Leaf 3 G I K K (J,K) Yes L Leaf 3 G I L L (K,L) Yes M Leaf 2 G M M M(I,M) (L,M) Yes N Leaf 1 N N N N (G,N) (M,N)

As illustrated in TABLE II, fifteen similarity values are used todetermine the value associated with the initial depth-first searchsequence. The fifteen similarity values consist of a similarity valuebetween stocks C and D, a similarity value between stocks C and E, asimilarity value between stocks B and F, a similarity value betweenstocks D and F, a similarity value between stocks E and F, a similarityvalue between stocks A and G, a similarity value between stocks F and H,a similarity value between stocks H and I, a similarity value betweenstocks H and J, a similarity value between stocks J and K, a similarityvalue between stocks K and L, a similarity value between stocks I and M,a similarity value between stocks L and M, a similarity value betweenstocks G and N, and a similarity value between stocks M and N. In thisexample, the function used to determine the value associated with theinitial depth-first search sequence is a sum of the fifteen similarityvalues.

TABLE III shows a modified sequence for considering child stocks of thebase stock in a depth-first search of the tree. The modified sequence isused to provide a second depth-first search for considering stocks inthe tree.

TABLE III Sequence for considering Stock child stocks S G A N A B F B CD C — D E E — F — G H I M H — I J K L J — K — L — M — N —

TABLE IV illustrates one embodiment of determining a value associatedwith the second depth-first search sequence. As illustrated, sixteensimilarity values are used to determine the value associated with thesecond depth-first search sequence. The sixteen similarity valuesconsist of a similarity value between stocks H and I, a similarity valuebetween stocks H and J, a similarity value between stocks J and K, asimilarity value between stocks K and L, a similarity value betweenstocks I and M, a similarity value between stocks L and M, a similarityvalue between stocks A and G, a similarity value between stocks B and M,a similarity value between stocks C and M, a similarity value betweenstocks C and D, a similarity value between stocks C and E, a similarityvalue between stocks B and F, a similarity value between stocks D and F,a similarity value between stocks E and F, a similarity value betweenstocks A and N, and a similarity value between stocks F and N. In thisexample, the function used to determine the value associated with thesecond depth-first search sequence is a sum of the sixteen similarityvalues.

TABLE IV At least Similarity one stock values used in previouslyInternal function considered? Stock or leaf? Level 1 2 3 4 evaluation NoG Internal 1 G — — — — No H Leaf 2 G H H H — Yes I Internal 2 G I H H(H,I) Yes J Leaf 3 G I J J (H,J) Yes K Leaf 3 G I K K (J,K) Yes L Leaf 3G I L L (K,L) Yes M Leaf 2 G M M M (I,M) (L,M) Yes A Internal 1 A M M M(A,G) Yes B Internal 2 A B M M (B,M) Yes C Leaf 3 A B C C (C,M) Yes DInternal 3 A B D C (C,D) Yes E Leaf 4 A B D E (C,E) Yes F Leaf 2 A F F F(B,F) (D,F) (E,F) Yes N Leaf 1 N N N N (A,N) (F,N)

The above process is repeated for additional depth-first searchsequences by modifying a sequence for considering child stocks for atleast one stock. Potential sequences for considering child stocks ofstock S are A-G-N, A-N-G, G-A-N, G-N-A, N-A-G and N-G-A. Potentialsequences for considering child stocks of stock A are B-F and F-B.Potential sequences for considering child stocks of stock B are C-D andD-C. Stock D has one potential sequence for considering its child,namely stock E. Potential sequences for considering child stocks ofstock G are H-I-M, H-M-I, I-H-M, I-M-H, M-H-I and M-I-H. Potentialsequences for considering child stocks of stock I are J-K-L, J-L-K,K-J-L, K-L-J, L-J-K and L-K-J.

Values associated with all possible depth-first search sequences of thetree may be determined. The number of possible depth-first searchsequences of the tree is equal to the product of 3!, 2!, 2!, 1!, 3! and3!, which equals 864.

Preferably, values associated with at most half of all possibledepth-first search sequences of the tree (432 in this example) aredetermined. In this case, the depth-first search sequences which areconsidered are limited by prohibiting reverse-equivalent sequences forconsidering child stocks of the root stock, namely the stock S. In thisexample, the sequences for considering child stocks of the stock S maybe limited to A-G-N, A-N-G and G-A-N (whose reverse-equivalent sequencesare N-G-A, G-N-A and N-A-G, respectively).

Based on the values, the depth-first search sequence having a maximumvalue associated therewith is determined. For purposes of illustrationand example, consider that the value associated with the seconddepth-first search sequence is greater than or equal to valuesassociated with the other depth-first search sequences. A signal may beoutputted to display a planar depiction of the tree in which the stocksare ordered according to the second depth-first search sequence.

A browsing sequence may be provided in accordance with the seconddepth-first search sequence. In this case, the sequence for browsing thestocks is G-H-I-J-K-L-M-A-B-C-D-E-F-N (see the second column of TABLEIV). The browsing sequence and the tree may be used in accordance withthe teachings in the patent application having Ser. No. 09/533,545, nowU.S. Pat. No. 6,460,033, which is hereby incorporated by reference intothe present disclosure.

In the above example, cyclic-equivalent and reverse cyclic-equivalentsequences may be prohibited by prohibiting cyclic-equivalent and reversecyclic-equivalent sequences for considering child stocks of the rootstock, namely the stock S. Thus, the sequence for considering childstocks of the stock S may be limited to A-G-N, since G-N-A and N-A-G arecyclic-equivalent sequences, and N-G-A, A-N-G and G-A-N are reversecyclic-equivalent sequences. In this case, the stocks from the firstnon-root stock to the first leaf stock may be considered twice todetermine a value associated with a depth-first search sequence of thetree. In the above example, the value associated with initialdepth-first search sequence would be further based upon a similarityvalue between stocks A and N, a similarity value between stocks B and N,and a similarity value between stocks C and N. The value associated withthe second depth-first search sequence would be further based upon asimilarity value between stocks G and N, and a similarity value betweenstocks H and N. However, as explained above, the second depth-firstsearch sequence would not have been considered.

An apparatus for performing embodiments of the herein-disclosed methodsand examples may comprise one or more programmed computers. Eachprogrammed computer may provide a particular functionality implementedusing hardware and/or software and/or firmware.

Preferably, a programmed computer includes a computer memory encodedwith executable instructions representing a computer program. Aprocessor is responsive to the computer memory to perform a series ofspecifically identified operations dictated by the computer program. Inthis way, the computer program can cause the computer to act in aparticular fashion.

Examples of the processor include, but are not limited to, a generalpurpose microprocessor, an application-specific integrated circuit(which may be either standard or custom), one or more discrete logicelements, a digital signal processor, an analog signal processor, one ormore circuits, or any combination thereof. It is noted that theprocessor may be embodied by either a single processing unit or aplurality of processing units. For example, the processor may beembodied by either a single, central processing unit or a plurality ofdistributed processing units.

Examples of the computer memory include, but are not limited to, anelectronic memory, a magnetic memory, an optical memory, and amagneto-optical memory. Examples of an electronic memory include, butare not limited to, a programmable electronic memory and a read-only,hard-wired electronic memory. Examples of a magnetic memory include, butare not limited to, a magnetic disk and a magnetic tape. The magneticdisk may be embodied by a magnetic floppy diskette or a magnetic harddrive, for example. Examples of an optical memory include, but are notlimited to, an optical disk. The optical disk may be embodied by acompact disk or a DVD, for example. Regardless of its form, the computermemory may be either read-only, once-writable, or rewritable.

In general, the processor may be responsive to any data structures,computer programs, and signals encoded on a computer-readable medium toperform an embodiment of any of the herein-disclosed methods andexamples. Examples of the computer-readable medium include, but are notlimited to, computer-readable storage media and computer-readablecommunication media. Examples of computer-readable storage media aredescribed with reference to the computer memory.

The computer is coupled to a display to display the visiblerepresentations described herein and other visible information to an enduser. Examples of the display include any of the herein-discloseddisplay devices.

The computer receives user input indicating a selection of a region fromone or more input devices. Examples of the one or more input devicesinclude, but are not limited to, a keyboard, a touch screen, a touchpad, a voice input device, and a pointing device (e.g. a mouse or apointing stick). The computer processes the user input and/orcommunicates at least one signal based upon the user input. The displayand the one or more input devices facilitate user interaction with thecomputer.

Embodiments of the herein-disclosed methods and examples can beperformed using either a single computer or a plurality of computers. Aplurality of computers may cooperate in a client-server fashion, forexample, wherein a server computer outputs at least one signal to causea client computer to display a visible representation and/or to providea user interface. The plurality of computers may cooperate in apeer-to-peer fashion, for example, wherein one peer computer acts as aserver computer and another peer computer acts as a client computer.

The teachings herein can be combined with and/or applied to any of theteachings in U.S. application Ser. No. 09/243,595, now U.S. Pat. No.6,359,635, which is hereby incorporated by reference in thisapplication.

As stated in the foregoing description, similarity values may be basedon a correlation between trading behaviors of two financial items. Otherexamples of trading behaviors include, but are not limited to, postingsof buy or sell messages such as those monitored by ThomsonFN.com'sI-Watch™ service. The messages may include super messages in whichbrokers/dealers specify whether they want to buy or sell a particularstock, a size of the particular stock to trade, and an exact price. Themessages may include interest messages which are not binding. Thesimilarity values may be equal to or otherwise based on a correlationcoefficient between the postings of messages for two financial items. Todetermine the correlation coefficient, a time series indicating anaggregate level of messaging for each of a plurality of time intervalsis determined for each stock. For example, the time series for a stockmay indicate the aggregate level of messaging within 5 minute or 20minute intervals, such as the data provided by ThomsonFN.com's I-Watch™service. The correlation coefficient is a cross correlation between twotime series for two different stocks.

Embodiments of the present invention have many practical applications. Afinancial Web site can display regions to show how a selected financialitem relates to other financial items. Each region may beuser-selectable so that a user can retrieve information associated withits corresponding financial item. Examples of the information include,but are not limited to, a quote, a bid price, an offer price, tradingvolume, a chart, technical analysis, fundamental analysis, historicalprices, profile information, research for the item, an income statement,a balance sheet, a prospectus, and a message board associated with theitem. Further, a selection of a region may facilitate (e.g. eitherassist or initiate) a transaction involving the financial item (e.g.buying or selling a stock).

The financial items represented by the regions may comprise either allfinancial items in an index (e.g. all stocks in the Standard & Poors'500 index), all financial items in a user's watch list, only the nearestneighbors to the base financial item based on the similarity values, allfinancial items in a market sector, or all financial items in a marketindustry, for example. Preferably, only financial items whosecorrelation with the base financial item is greater than zero areconsidered for inclusion in the graphical representation. Optionally, acriterion for including financial items in the graphical representationmay be more stringent, for example, only financial items whosecorrelation with the base financial item is greater than a positivethreshold (e.g. 0.2, 0.5, 0.7) are considered for inclusion in thegraphical representation.

A mutual fund family may determine how a family of its mutual funds areinterrelated based on similarity values. The relationship can bedisplayed on the mutual fund family's Web site, displayed in an on-lineversion of a prospectus or an annual report, and/or printed in a hardcopy prospectus or an annual report.

The regions may be displayed with different display properties based ona property of the items. Examples of the property include, but are notlimited to, a sector within which the item is contained, an industrywithin which the item is contained, a change in price of the item, achange in volume of the item, a percentage of institutional volume, apercentage of retail volume, and an amount of institutional interest tobuy or sell based on super messages and/or interest messages. Examplesof the display property include, but are not limited to, an interiorcolor of the region, an interior gray scale of the region, an interiorfill pattern for the region, a color bordering the region, an imagewithin the region, and text within the region. For example, the colormay be a shade of green if the price is up, and a shade of red if theprice is down.

A sequence of the same graphical representation but with time-varyingdisplay properties can be displayed in succession to provide an animatedview of the financial information over the course of a time period. Forexample, a recap of the price action of each financial item over thecourse of a day can be shown in an animation that lasts much less time(e.g. under 30 seconds, or under a minute). In this case, the colors ofthe regions may change during the animation to reflect price changes inthe respective items over the course of the day.

By selecting the base item to be a newsworthy stock or anothernewsworthy financial item, a financial news service can show how buyingor selling pressure in the newsworthy item is spreading to other stocksor financial items. The financial news service may be either atelevision broadcast service, a computer network service such as anInternet service, a wireless telephone or PDA service, or a printservice.

By allowing a user to select the base item, the user can be provided aview of the stock market that focuses on his/her own stock of interest.For example, a user may select the base item to be a stock that the usereither owns, shorts, is interested in buying, or is interested inselling.

Optionally, the weight value of each item can be based on the similarityvalue between the item and the base item. In this case, items which arehighly-similar to the base item are displayed with greater areas thanitems which are less similar to the base item. Each weight value can bebased on or equal to the correlation between the corresponding item andthe base item. For example, the weight value may be equal to thecorrelation if the correlation is greater than zero. As another example,the weight value may be equal to the correlation squared if thecorrelation is greater than zero. In both of the above examples, an itemwould not be considered for and included in the graphical representationif its correlation with the base item is less than or equal to zero.

FIG. 11 is a first example of regions to represent the fourteen stocksA-N and the base stock S related by the tree in FIG. 10. In thisexample, the regions all have approximately the same area. The regionsprovide a 360-degree view of the stocks A-N and S.

FIG. 12 is a second example of regions to represent the fourteen stocksA-N and the base stock S related by the tree in FIG. 10. In thisexample, each region has an area based on a similarity value between itscorresponding stock and the base stock. Thus, if a first stock (e.g.stock A) is more similar to the base stock S than a second stock (e.g.stock B) is to the base stock S, the region representing stock A has agreater area than the region representing stock B. The regions provide a360-degree view of the stocks A-N and S.

It will be apparent to those skilled in the art that the disclosedinventions may be modified in numerous ways and may assume manyembodiments other than the preferred forms specifically set out anddescribed herein. For example, the acts described with reference toFIGS. 1-4 and 6-8 may be executed in an order other than that indicatedin FIGS. 1-4 and 6-8. Further, some of the acts may be performed inparallel. Still further, the regions may have the herein-disclosedsimilarity-based areas but with non-annulus-sector shapes in alternativeembodiments.

Accordingly, it is intended by the appended claims to cover allmodifications which fall within the true spirit and scope of the presentinvention.

What is claimed is:
 1. A non-transitory computer-readable storage mediumencoded with a computer program to cause a computer to display, using adisplay device, a visible representation of a plurality of stocks in astock market by a respective plurality of regions that are arrangedbased on a plurality of similarity values between a respective pluralityof pairs of the stocks in the stock market, wherein each of theplurality of similarity values is based on a respective correlationbetween a respective first time series indicating, for each of aplurality of time intervals, an aggregate level of messaging in postingsof messages for a respective first stock in its respective pair of thestocks and a respective second time series indicating, for each of theplurality of time intervals, an aggregate level of messaging in postingsof messages for a respective second stock in its respective pair of thestocks, and wherein each of the plurality of regions is user-selectableto retrieve information from a message board associated with itsrespective one of the plurality of stocks.
 2. The non-transitorycomputer-readable storage medium of claim 1 wherein the plurality ofregions are arranged to optimize a function of at least two similarityvalues between a respective at least two pairs of the stocks representedby a respective at least two angularly-adjacent pairs of the pluralityof regions.
 3. The non-transitory computer-readable storage medium ofclaim 1 wherein the function is a minimum of the at least two similarityvalues between the respective at least two pairs of the stocksrepresented by the respective at least two angularly-adjacent pairs ofthe plurality of regions, and wherein the plurality of regions arearranged to maximize the function.
 4. The non-transitorycomputer-readable storage medium of claim 1 wherein the computer programfurther is to cause the computer to initiate a transaction involving oneof the plurality of stocks.
 5. An apparatus comprising: a displaydevice; and a computer coupled to the display device, the computerprogrammed to cause the display device to display a visiblerepresentation of a plurality of stocks in a stock market by arespective plurality of regions that are arranged based on a pluralityof similarity values between a respective plurality of pairs of thestocks in the stock market, wherein each of the plurality of similarityvalues is based on a respective correlation between a respective firsttime series indicating, for each of a plurality of time intervals, anaggregate level of messaging in postings of messages for a respectivefirst stock in its respective pair of the stocks and a respective secondtime series indicating, for each of the plurality of time intervals, anaggregate level of messaging in postings of messages for a respectivesecond stock in its respective pair of the stocks, and wherein each ofthe plurality of regions is user-selectable to retrieve information froma message board associated with its respective one of the plurality ofstocks.
 6. The apparatus of claim 5 wherein the plurality of regions arearranged to optimize a function of at least two similarity valuesbetween a respective at least two pairs of the stocks represented by arespective at least two angularly-adjacent pairs of the plurality ofregions.
 7. The apparatus of claim 5 wherein the function is a minimumof the at least two similarity values between the respective at leasttwo pairs of the stocks represented by the respective at least twoangularly-adjacent pairs of the plurality of regions, and wherein theplurality of regions are arranged to maximize the function.
 8. Theapparatus of claim 5 wherein the computer is programmed to initiate atransaction involving one of the plurality of stocks.
 9. A methodcomprising: displaying, using a display device, a visible representationof a plurality of stocks in a stock market by a respective plurality ofregions that are arranged based on a plurality of similarity valuesbetween a respective plurality of pairs of the stocks in the stockmarket, wherein each of the plurality of similarity values is based on arespective correlation between a respective first time seriesindicating, for each of a plurality of time intervals, an aggregatelevel of messaging in postings of messages for a respective first stockin its respective pair of the stocks and a respective second time seriesindicating, for each of the plurality of time intervals, an aggregatelevel of messaging in postings of messages for a respective second stockin its respective pair of the stocks, and wherein each of the pluralityof regions is user-selectable to retrieve information from a messageboard associated with its respective one of the plurality of stocks. 10.The method of claim 9 wherein the plurality of regions are arranged tooptimize a function of at least two similarity values between arespective at least two pairs of the stocks represented by a respectiveat least two angularly-adjacent pairs of the plurality of regions. 11.The method of claim 9 wherein the function is a minimum of the at leasttwo similarity values between the respective at least two pairs of thestocks represented by the respective at least two angularly-adjacentpairs of the plurality of regions, and wherein the plurality of regionsare arranged to maximize the function.
 12. The method of claim 9 furthercomprising initiating a transaction involving one of the plurality ofstocks.